1. Technical Field
In general, the present disclosure relates to nuclear medical imaging. More particularly, the disclosure relates to Positron Emission Tomography (PET) imaging and accurate estimation of scintillation crystal efficiency of PET detector blocks in a PET system.
2. General Background of the Invention
Nuclear medicine is a unique specialty wherein radiation emission is used to acquire images that show the function and physiology of organs, bones or tissues of the body. The technique of acquiring nuclear medicine images entails first introducing radiopharmaceuticals into the body—either by injection or ingestion. These radiopharmaceuticals are attracted to specific organs, bones, or tissues of interest. Upon arriving at their specified area of interest, the radiopharmaceuticals produce gamma photon emissions, which emanate from the body and are then captured by a scintillation crystal. The interaction of the gamma photons with the scintillation crystal produces flashes of light, which are referred to as “events.” Events are detected by an array of photo detectors (such as photomultiplier tubes), and their spatial locations or positions are then calculated and stored. In this way, an image of the organ or tissue under study is created from detection of the distribution of the radioisotopes in the body.
One particular nuclear medicine imaging technique is known as positron emission tomography, or PET. PET is used to produce images for diagnosing the biochemistry or physiology of a specific organ, tumor or other metabolically active site. The measurement of tissue concentration using a positron emitting radionuclide is based on coincidence detection of the two gamma photons arising from a positron annihilation. When a positron is annihilated by an electron, two 511 keV gamma photons are simultaneously produced and travel in approximately opposite directions. Gamma photons produced by an annihilation event can be detected by a pair of oppositely disposed radiation detectors capable of producing a signal in response to the interaction of the gamma photons with a scintillation crystal. Annihilation events are typically identified by a time coincidence between the detection of the two 511 keV gamma photons in the two oppositely disposed detectors; i.e., the gamma photon emissions are detected virtually simultaneously by each detector. When two oppositely disposed gamma photons each strike an oppositely disposed detector to produce a time coincidence event, they also identify a line-of-response (LOR) along which the annihilation event has occurred. An example of a PET method and apparatus is described in U.S. Pat. No. 6,858,847, which patent is incorporated herein by reference in its entirety.
FIG. 1 is a graphic representation of a line of response. An annihilation event 140 occurring in imaged object mass 130 may emit two simultaneous gamma rays (not shown) traveling substantially 180° apart. The gamma rays may travel out of scanned mass 130 and may be detected by block detectors 110A and 110B, where the detection area of the block detector defines the minimum area or maximum resolution within which the position of an incident gamma ray may be determined. Since block detectors 110A and 110B are unable to determine precisely where the gamma rays were detected within this finite area, the LOR 120 connecting block detectors 110A and 110B may actually be a tube with its radius equal to the radius of block detectors 110A and 110B. Similar spatial resolution constraints are applicable to other types of detectors, such as photomultiplier tubes.
To minimize data storage requirements, clinical projection data are axially compressed, or mashed, to within a predetermined span. With a cylindrical scanner, which has translational symmetry, the geometrical blurring resulting from axial compression may be modeled by projecting a blurred image into LOR space, followed by axial compression. This eliminates the storage of the axial components, and special algorithms have been developed to incorporate system response. The system response modeling then will allow the use of standard reconstruction algorithms such as Joseph's Method, and a reduction of data storage requirements.
The LOR defined by the coincidence events are used to reconstruct a three-dimensional distribution of the positron-emitting radionuclide within the patient. In two-dimensional PET, each 2D transverse section or “slice” of the radionuclide distribution is reconstructed independently of adjacent sections. In fully three-dimensional PET, the data are sorted into sets of LOR, where each set is parallel to a particular detector angle, and therefore represents a two dimensional parallel projection p(s, Φ) of the three dimensional radionuclide distribution within the patient, where “s” corresponds to the displacement of the imaging plane perpendicular to the scanner axis from the center of the gantry, and “Φ” corresponds to the angle of the detector plane with respect to the x axis in (x, y) coordinate space (in other words, Φ corresponds to a particular LOR direction).
Coincidence events are integrated or collected for each LOR and stored in a sinogram. In this format, a single fixed point in f(x, y) traces a sinusoid in the sinogram. In each sinogram, there is one row containing the LOR for a particular azimuthal angle Φ; each such row corresponds to a one-dimensional parallel projection of the tracer distribution at a different coordinate along the scanner axis. This is shown conceptually in FIG. 2.
It is known that the efficiency of the crystals in the detector modules or blocks of a PET scanner will vary from crystal to crystal in terms of luminescence per gamma strike. Therefore, it is important to estimate accurately the crystal efficiency of each detector in order to obtain good normalization for 3D PET data. Inaccurate knowledge of crystal efficiency can lead to artifacts, higher noise in the image, and/or poor uniformity in the reconstructed image.
Normalization factors are corrections that compensate for non-uniformity of the PET detector pair efficiencies. A component-based method is commonly used to improve accuracy of the normalization factors. Most components, such as geometrical and crystal interference components, can be estimated or computed in advance for a particular scanner type. The crystal efficiency component must be estimated relatively often for each physical scanner.
Recent developments in clinical PET systems allow the measurement of the time-of-flight (TOF) difference between the two coincident photons with a resolution as low as 500 ps full-width half-maximum (FWHM). This leads to improved signal-to-noise ratio when large patients are imaged. The TOF acquisition results in additional data dimensions that significantly increase data size. One practical approach is to use list mode reconstruction. However, such reconstruction depends on the number of registered events and therefore is time consuming for high count studies. This reconstruction is also possible with only certain algorithms. On the other hand, due to the redundancy of TOF information, data can be compressed without loss of resolution. Such compression may consist of axial rebinning and azimuthal mashing. These are useful if reconstruction algorithms from histogrammed data are exploited. Then reconstruction time is independent of acquisition time and any reconstruction algorithms are ready to be used.
In a clinical environment, normalization data are acquired with the same hardware TOF rebinner on a regular basis. TOF compression complicates basic equations behind the normalization data model and special methods should be developed to estimate normalization components.
A number of methods to estimate crystal efficiencies from uncompressed LOR or list mode normalization data have been developed. For example, the method from Defrise is an analytical method that typically uses only part of the available data. The fan sum method, which tries to use all available data, is not exact and might lead to bias in a very uneven efficiency distribution. Analytical methods allow for fast processing of data. However, this method might not be required in the clinical environment, where the normalization scan is performed separately and there is enough time to process it.
On the other hand, crystal efficiencies can be estimated by the Maximum Likelihood (ML) approach. The iterative approach has the advantage of versatility, where all available data are easily accommodated. It is truly a statistical method. Moreover, normalization scan object position can be more or less arbitrary. This approach results in solving a large system of nonlinear equations iteratively, and is consequently potentially resource consuming.
TOF compression complicates basic equations behind the normalization data model. Therefore, the mentioned methodologies are not directly applicable. Accordingly, there is a need in the art for a method for estimating crystal efficiency components from TOF compressed normalization data.